Attempts to produce artificial intelligence-based diagnostic decision support systems for the task of diagnosis have failed for many reasons. In some cases, the presentation of patients is so widely-varied that it is difficult for an artificial intelligence system to adequately represent the diversity of the phenomena that characterize each condition. For these, the sensitivity and specificity are low, as are the positive predictive value (PPV) and negative predictive value (NPV). In other cases, the non-sequential evolution of a clinical condition, with periods of exacerbation and remission, leads to an intermittency of features such that various predicates associated with the condition are frequently absent, such that the resulting predictive model or system experiences an excessive rate of false-negative determinations in persons who do indeed have the condition.
In other cases, the severity or frequency of the condition exhibits a wide range, and a system that is capable of detecting severe or frequent disease is not adequately capable of recognizing less-severe instances of the same disease. In yet other cases, the number of features needed to produce a system with adequate statistical sensitivity and specificity is so large that it is not practical (for reasons of time, expense, or other factors) to expect any clinician or set of clinicians to supply non-null values for all of, or a sufficient number of, the features required in a fashion that adds to their workload or intrudes upon and disrupts their customary workflow patterns. In still other cases, the style and mode of the system's interaction with the clinician users interferes with the credentials-based, fiduciary role that the clinician has with regard to the patient's care; the system may have less information upon which to base its conclusions or advice, yet it nonetheless acts in a way that may contradict determinations that the clinician has already reached, appearing to countermand the authority and responsibility that lodges with the clinician and, perhaps, augmenting the clinician's risk of medical malpractice claims or other exposures.
In other cases, the decision support system's operations were slow or logistically discordant with the conduct of the care services process, such that the advice provided by the system was tardy, delivered too late to be of use for prevention or therapeutic decisions. Ex post corroboration of decisions that have already been made is of very low value, but ex post discorroboration of decisions that cannot be amended, undone, or redone is of negative value and vehemently disliked.
In yet other cases, the decision support system is only suited to one-time application, assisting in resolving a diagnosis at the time of presentation, and is not amenable to repeated, ongoing application in the care of a patient over time, as certain conditions that were active become suspended or inactive or cured while other new conditions supervene and become active or previously-suspended ones become reactivated.
Despite long and intense effort, to date no broadly effective approach to automatically recommend nosologic entities or conditions based on the content of unstructured clinical narrative has yet appeared.